Abstract
The COVID-19 pandemic has had an obliterating impact on the health and well-being of the worldwide populace. It has recently become one of the most severe and acute diseases and has spread globally. Therefore, an automated detection system should be implemented as the fastest diagnostic option to control the spread of COVID-19. This study aims to introduce neural network-based strategies for classifying and detecting COVID-19 early through image processing utilizing X-ray images. Despite the extensive use of directly fed X-ray images into the classifier, there is a lack of comparative analysis between the feature-based system and the direct imaging system in the classification of COVID-19 to evaluate the efficiency of feature extraction methods. Therefore, the proposed system represents introductory experiments of feature-based system using image Feature Extraction Algorithms [Texture, Grey-Level Co-occurrence Matrix (GLCM), Grey-Level Dependence Matrix (GLDM), Fast Fourier Transform (FFT), and Discrete Wavelet Transform (DWT)], Dimensionality Reduction Algorithms (Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Sparse Autoencoder, and Stacked Autoencoder), Feature Selection Algorithms (Anova F-measure, Chi-square Test, and Random Forest), and Neural Networks [Feed-Forward Neural Network (FFNN) and Convolutional Neural Network (CNN)] on generated datasets consisting of Normal, COVID-19, and Pneumonia-infected chest X-ray images. Tenfold Cross-Validations are implemented during the classification process. A baseline model is created where no Feature Extraction Algorithm is implemented. Accuracy, sensitivity, specificity, precision, and F-measure metrics are utilized to assess classification performance. Finally, a comprehensive comparison of the success of the Feature-based system (Feature Extraction Algorithms, Dimensionality Reduction Algorithms, and Feature Selection Algorithms) in COVID-19 classification from X-ray images is made with the baseline model. The highest classification accuracy (95.44 ± 0.03%), sensitivity (96%), specificity (98%), precision (96%), and F-measure (96%) are achieved for Feature-based systems using Principal Component Analysis. The aim is to lay the foundation for the potential creation of a system that can automatically distinguish COVID-19 infection based on chest X-ray images using the Feature-based model.
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Data availability
The datasets generated during the current study are available from the corresponding author on reasonable request.
References
Abdul Salam M, Taha S, Ramadan M (2021) COVID-19 detection using federated machine learning. PLoS ONE 16(6):e0252573
Agrawal S, Honnakasturi V, Nara M, Patil N (2023) Utilizing deep learning models and transfer learning for COVID-19 detection from X-ray images. SN Comput Sci 4(4):326
Ahammed K, Satu MS, Abedin MZ, Rahaman MA, Islam SMS (2020) Early detection of coronavirus cases using chest X-ray images employing machine learning and deep learning approaches. MedRxiv. https://doi.org/10.13140/RG.2.2.13579.11045
Ahmadi N, Akbarizadeh G (2020) Iris tissue recognition based on GLDM feature extraction and hybrid MLPNN-ICA classifier. Neural Comput Appl 32(7):2267–2281. https://doi.org/10.1007/s00521-018-3754-0
Ahsan MM, Uddin MR, Farjana M, Sakib AN, Momin KA, Luna SA (2022) Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG-16. arXiv preprint arXiv:2206.01862. Accessed date 25 April 2023
Akl AA, Hosny KM, Fouda MM, Salah A (2023) A hybrid CNN and ensemble model for COVID-19 lung infection detection on chest CT scans. PLoS ONE 18(3):0282608
Anuradha B, Reddy VV (2008) ANN for classification of cardiac arrhythmias. ARPN J Eng Appl Sci 3(3):1–6
Apostolopoulos ID, Mpesiana TA (2020) Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 43(2):635–640. https://doi.org/10.1007/s13246-020-00865-4
Avinash S, Naveen Kumar HN, Guru Prasad MS, Mohan Naik R, Parveen G (2023) Early detection of malignant tumor in lungs using feed-forward neural network and K-nearest neighbor classifier. SN Comput Sci 4(2):195
BabaAhmadi A, Khalafi S, ShariatPanahi M, Ayati M (2023) Designing an improved deep learning-based model for COVID-19 recognition in chest x-ray images: a knowledge distillation approach. arXiv preprint arXiv:2301.02735.
Bachri OS, Kusnadi HM, Nurhayati OD (2017) Feature selection based on CHI square in artificial neural network to predict the accuracy of student study period. Int J Civil Eng Technol 8(8):731–739
Baheti P, Sikka M, Arya KV, Rajesh, R (2020) Federated learning on distributed medical records for detection of lung nodules. In VISIGRAPP. 4: VISAPP. p 445–451.
Bandyopadhyay H, Dastidar SG, Mondal B, Banerjee B, Das N (2021) A distillation based approach for the diagnosis of diseases. arXiv preprint arXiv:2108.03470. Accessed date 03 May 2023.
Belgiu M, Drăgu L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
Bhattacharya A, Gawali M, Seth J, Kulkarni V (2022) Application of Federated Learning in building a robust COVID-19 Chest X-ray classification Model. arXiv preprint arXiv:2204.10505. Accessed date 03 May 2023.
Bian G, Qu W, Shao B (2023) Blockchain-based trusted federated learning with pre-trained models for COVID-19 detection. Electronics 12(9):2068
Brisimi TS, Chen R, Mela T, Olshevsky A, Paschalidis IC, Shi W (2018) Federated learning of predictive models from federated electronic health records. Int J Med Inf 112:59–67
Brodić D, Amelio A, Milivojević ZN (2017) Clustering documents in evolving languages by image texture analysis. Appl Intell 46(4):916–933
Castleman KR (1996) Digital image processing. Prentice Hall Press, Hoboken
Chaddad A, Hassan L, Desrosiers C (2021) Deep CNN models for predicting COVID-19 in CT and x-ray images. J Med Imaging 8(S1):014502–014502
Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, Xia J, Yu T, Zhang X, Zhang L (2020) Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The Lancet 395(10223):507–513. https://doi.org/10.1016/S0140-6736(20)30211-7
COVID-19 Radiography database. https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database. Accessed 20 February 2022.
Danala G, Thai T, Gunderson CC, Moxley KM, Moore K, Mannel RS, Liu H, Zheng B, Qiu Y (2017) Applying quantitative CT image feature analysis to predict response of ovarian cancer patients to chemotherapy. Acad Radiol 24(10):1233–1239. https://doi.org/10.1016/j.acra.2017.04.014
Das AK, Ghosh S, Thunder S, Dutta R, Agarwal S, Chakrabarti A (2021) Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network. Pattern Anal Appl 24:1111–1124
Dasha P, Parhi SS (2023) Federated model learning for COVID-19 screening from chest X-ray images. Appl Soft Comput 109:107333
Fauvel M, Chanussot J, Benediktsson JA (2006) Kernel principal component analysis for feature reduction in hyperspectrale images analysis. In: 7th Nordic Signal Processing Symposium (NORSIG). p 238–241. https://doi.org/10.1109/NORSIG.2006.275232
Feki I, Ammar S, Kessentini Y, Muhammad K (2021) Federated learning for COVID-19 screening from chest X-ray images. Appl Soft Comput 106:107330
Foysal M, Hossain ABM, Yassine A, Hossain MS (2023) Detection of COVID-19 Case from Chest CT Images Using Deformable Deep Convolutional Neural Network. J Healthcare Eng. 2023:01–12. https://doi.org/10.1155/2023/4301745
Ghosh R (2021) Determining top fully connected layer’s hidden neuron count for transfer learning, using knowledge distillation: a case study on chest X-ray classification of pneumonia and COVID-19. J Digit Imaging 34(6):1349–1358
Göhler F, Corman VM, Bleicker T, Stroux A, Dewey M, Diekhoff T (2022) Contamination of CT scanner surfaces with SARS-CoV-2 and infective potential after examination of invasively ventilated, non-invasively ventilated and non-ventilated patients with positive throat swabs: prospective investigation using real-time reverse-transcription PCR and viral cell culture. Insights Imaging 13(1):1–9. https://doi.org/10.1186/s13244-022-01202-x
Gou J, Yu B, Maybank SJ, Tao D (2021) Knowledge distillation: a survey. Int J Comput Vision 129:1789–1819
Guan Q, Wang Y, Ping B, Li D, Du J, Qin Y, Lu H, Wan X, Xiang J (2019) Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study. J Cancer 10(20):4876–4882. https://doi.org/10.7150/jca.28769
Guefrechi S, Jabra MB, Ammar A, Koubaa A, Hamam H (2021) Deep learning based detection of COVID-19 from chest X-ray images. Multimedia Tools Appl 80:31803–31820
Gupta K, Bajaj V (2023) Deep learning models-based CT-scan image classification for automated screening of COVID-19. Biomed Signal Process Control 80:104268
Gupta S, Mishra A, Menaka R (2015) Ischemic stroke detection using image processing and ANN. In: IEEE International Conference on Advanced Communication, Control and Computing Technologies (ICACCCT). p 1416–1420. https://doi.org/10.1109/ICACCCT.2014.7019334
Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 6:610–621
Ho TT, Tran KD, Huang Y (2022) FedSGDCOVID: federated SGD COVID-19 detection under local differential privacy using chest X-ray images and symptom information. Sensors 22(10):3728
Hu S, Xu C, Guan W, Tang Y, Liu Y (2014) Texture feature extraction based on wavelet transform and gray-level co-occurrence matrices applied to osteosarcoma diagnosis. Bio-Med Mater Eng 24(1):129–143
Huang ML, Liao YC (2022) A lightweight CNN-based network on COVID-19 detection using X-ray and CT images. Comput Biol Med 146:105604
Huang L, Shea AL, Qian H, Masurkar A, Deng H, Liu D (2019) Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. J Biomed Inform 99:103291
Islam MZ, Islam MM, Asraf A (2020) A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Inf Med Unlocked 20:100412. https://doi.org/10.1016/j.imu.2020.100412
Jadhav SM, Nalbalwar SL, Ghatol AA (2011) Artificial neural network based cardiac arrhythmia disease diagnosis. In: International Conference on Process Automation, Control and Computing (PACC). p 1–6. https://doi.org/10.1109/PACC.2011.5979000
Jia W, Sun M, Lian J, Hou S (2022) Feature dimensionality reduction: a review. Complex & Intelligent Systems. p 1–31. https://doi.org/10.1007/s40747-021-00637-x
Jiang ZP, Liu YY, Shao ZE, Huang KW (2021) An improved VGG-16 model for pneumonia image classification. Appl Sci 11(23):11185
Jollife IT, Cadima J (2016) Principal component analysis: a review and recent developments. Philos Trans R Soc a Math, Phys Eng Sci 374(2065):20150202. https://doi.org/10.1098/rsta.2015.0202
Karahaliou A, Skiadopoulos S, Boniatis I, Sakellaropoulos P, Likaki E, Panayiotakis G, Costaridou L (2007) Texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis. Br J Radiol 80(956):648–656
Kathamuthu ND, Subramaniam S, Le QH, Muthusamy S, Panchal H, Sundararajan SCM, Alrubaie AJ, Zahra MMA (2023) A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications. Adv Eng Softw 175:103317
Kaur M, Sakhare SR, Wanjale K, Akter F (2022) Early stroke prediction methods for prevention of strokes. Behav Neurol. https://doi.org/10.1155/2022/7725597
Khan SU, Islam N, Jan Z, Haseeb K, Shah SIA, Hanif M (2022) A machine learning-based approach for the segmentation and classification of malignant cells in breast cytology images using gray level co-occurrence matrix (GLCM) and support vector machine (SVM). Neural Comput Appl 34(11): 8365-8372. https://doi.org/10.1007/S00521-021-05697-1.
Kim YG, Kim K, Wu D, Ren H, Tak WY, Park SY, Lee YR, Kang MK, Park JG, Kim BS, Chung WJ, Kalra MK, Li Q (2022) Deep learning-based four-region lung segmentation in chest radiography for COVID-19 diagnosis. Diagnostics 12(1):101. https://doi.org/10.3390/diagnostics12010101
Kroft LJM, Van Der Velden L, Girón IH, Roelofs JJH, De Roos A, Geleijns J (2019) Added value of ultra-low-dose computed tomography, dose equivalent to chest x-ray radiography, for diagnosing chest pathology. J Thorac Imaging 34(3):179–186. https://doi.org/10.1097/RTI.0000000000000404
Kumar A, Pang GK (2002) Defect detection in textured materials using Gabor filters. IEEE Trans Ind Appl 38(2):425–440
Lee J, Pant SR, Lee HS (2015) An adaptive histogram equalization based local technique for contrast preserving image enhancement. Int J Fuzzy Logic Intell Syst 15(1):35–44. https://doi.org/10.5391/ijfis.2015.15.1.35
Li C, Yang Y, Liang H, Wu B (2021) Transfer learning for establishment of recognition of COVID-19 on CT imaging using small-sized training datasets. Knowl-Based Syst 218:106849
Li G, Togo R, Ogawa T, Haseyama M (2022) Dataset distillation for medical dataset sharing. arXiv preprint arXiv:2209.14603. Accessed date 27 April 2023.
Liu B, Yan B, Zhou Y, Yang Y, Zhang Y (2020) Experiments of federated learning for covid-19 chest x-ray images. arXiv preprint arXiv:2007.05592. Accessed date 29 April 2023.
Loey M, Smarandache F, Khalifa NEM (2020) Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning. Symmetry 12(4):651. https://doi.org/10.3390/SYM12040651
Maghdid, HS, Asaad AT, Ghafoor KZ, Sadiq AS, Mirjalili S, Khan MK (2021, April) Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. In Multimodal image exploitation and learning 2021 (Vol. 11734, pp. 99-110). SPIE.
Makkar A, Santosh KC (2023) SecureFed: federated learning empowered medical imaging technique to analyze lung abnormalities in chest X-rays. Int J Mach Learn Cybern 14(8): 2659–2670. https://doi.org/10.1007/s13042-023-01789-7.
Mapayi T, Viriri S, Tapamo JR (2015) Adaptive thresholding technique for retinal vessel segmentation based on GLCM-energy information. Comput Math Methods Med.
McMahan B, Moore E, Ramage D, Hampson S, Arcas YBA (2017, April) Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (Vol. 54, pp. 1273-1282). Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR
Mohammed SN, Alkinani FS, Hassan YA (2020) Automatic computer aided diagnostic for COVID-19 based on chest X-Ray image and particle swarm intelligence. Int J Intell Eng Syst. 13(5):63–73. https://doi.org/10.22266/ijies2020.1031.07
Narin A, Kaya C, Pamuk Z (2021) Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Anal Appl 24(3):1207–1220. https://doi.org/10.1007/s10044-021-00984-y
Ng MY, Lee EYP, Yang J, Yang F, Li X, Wang H, Lui MMS, Lo CSY, Leung B, Khong PL, Hui CKM, Yuen KY, Kuo MD (2020) Imaging profile of the covid-19 infection: radiologic findings and literature review. Radiol Cardiothorac Imaging. https://doi.org/10.1148/ryct.2020200034
Novitasari DCR, Lubab A, Sawiji A, Asyhar AH (2019) Application of feature extraction for breast cancer using one order statistic, GLCM, GLRLM, and GLDM. Adv Sci, Technol Eng Syst J. 4(4):115–120
Ozturk T, Talo M, Azra E, Baran U, Yildirim O (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 121:103–792. https://doi.org/10.1016/j.compbiomed.2020.103792
Öztürk Ş, Özkaya U, Barstuğan M (2021) Classification of coronavirus (COVID-19) from X-ray and CT images using shrunken features. Int J Imaging Syst Technol 31(1):5–15. https://doi.org/10.1002/ima.22469
Pang S, Meng F, Wang X, Wang J, Song T, Wang X, Cheng X (2020) VGG-16-T: a novel deep convolutional neural network with boosting to identify pathological type of lung cancer in early stage by ct images. Int J Comput Intell Systems 13(1):771–780. https://doi.org/10.2991/ijcis.d.200608.001
Pantic I, Dacic S, Brkic P, Lavrnja I, Pantic S, Jovanovic T, Pekovic S (2014) Application of fractal and grey level co-occurrence matrix analysis in evaluation of brain corpus callosum and cingulum architecture. Microsc Microanal 20(5):1373–1381
Park SY, Kim K, Woo SH, Park JT, Jeong S, Kim J, Hong S (2021) Artificial neural network approach for acute poisoning mortality prediction in emergency departments. Clin Exp Emerg Med. 8(3):229–236. https://doi.org/10.15441/ceem.20.113
Patil A, Rane M (2021) Convolutional neural networks: an overview and its applications in pattern recognition. Smart Innov Syst Technol 195:21–30. https://doi.org/10.1007/978-981-15-7078-0_3
Pham MT, Lefèvre S, Merciol F (2018) Attribute profiles on derived textural features for highly textured optical image classification. IEEE Geosci Remote Sens Lett 15(7):1125–1129
Ponmalar A, Nokudaiyaval G, Vishnu Kirthiga R, Pavithra P, Sri Rakshya RVT (2021) Stroke Prediction System Using Artificial Neural Network. In: 6th International Conference on Communication and Electronics Systems (ICCES). p 1898–1902. https://doi.org/10.1109/ICCES51350.2021.9489055
Prasad BVP, Parthasarathy V (2018) Detection and classification of cardiovascular abnormalities using FFT based multi-objective genetic algorithm. Biotechnol Biotechnol Equip 32(1):183–193. https://doi.org/10.1080/13102818.2017.1389303
Ragab DA, Attallah O (2020) FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features. PeerJ Comput Sci 6:1–30. https://doi.org/10.7717/peerj-cs.306
Raghupathi V, Raghupathi W (2017a) Preventive healthcare: a neural network analysis of behavioral habits and chronic diseases. Healthcare 5(1):1–13. https://doi.org/10.3390/healthcare5010008
Raghupathi V, Raghupathi W (2017b) Preventive healthcare: a neural network analysis of behavioral habits and chronic diseases. Healthcare 5(1):8 (MDPI)
Rai HM, Chatterjee K (2018) A novel adaptive feature extraction for detection of cardiac arrhythmias using hybrid technique MRDWT & MPNN classifier from ECG big data. Big Data Research 12:13–22
Rajakumari R, Kalaivani L (2020) Abnormality detection and classification using artificial neural network. Int J Sci Technol Res 9(3):1234–1237
Rajpal S, Agarwal M, Rajpal A, Lakhyani N, Saggar A, Kumar N (2020) COV-ELM classifier: An extreme learning machine based identification of COVID-19 using chest X-ray images. arXiv:2007.08637. Accessed date 20 January 2022.
Reddy BB, Sudhakar MV, Reddy PR, Reddy PR (2023) Ensemble deep honey architecture for COVID-19 prediction using CT scan and chest X-ray images. Multimed Syst. p 1–27. https://doi.org/10.1007/S00530-023-01072-3
Rosenfeld A (1976) Digital picture processing. Academic press, Cambridge
Saood A, Hatem I (2021) COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Med Imaging 21(1):1–10. https://doi.org/10.1186/s12880-020-00529-5
Saygılı A (2021) A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods. Appl Soft Comput 105:107323
Sethy PK, Behera SK, Anitha K, Pandey C, Khan MR (2021) Computer aid screening of COVID-19 using X-ray and CT scan images: an inner comparison. J Xray Sci Technol 29(2):197–210
Setiono R (1996) Extracting rules from pruned neural networks for breast cancer diagnosis. Artif Intell Med 8(1):37–51. https://doi.org/10.1016/0933-3657(95)00019-4
Singh S, Srivastava D, Agarwal S (2017) GLCM and its application in pattern recognition. In: 5th International Symposium on Computational and Business Intelligence (ISCBI). p 20–25. https://doi.org/10.1109/ISCBI.2017.8053537
Sthle L, Wold S (1989) Analysis of variance (ANOVA). Chemom Intell Lab Syst 6(4):259–272. https://doi.org/10.1016/0169-7439(89)80095-4
Stone M (1978) Cross-validation: a review. Stat A J Theor Appl Stat. 9(1):127–139. https://doi.org/10.1080/02331887808801414
Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650
Taver V, Johannet A, Borrell-Estupina V, Pistre S (2015) Feed-forward vs recurrent neural network models for non-stationarity modelling using data assimilation and adaptivity. Hydrol Sci J 60(7–8):1242–1265. https://doi.org/10.1080/02626667.2014.967696
Toraman S, Burak T, Turkoglu I (2020) Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks. Chaos Solitons Fractals 140:110122. https://doi.org/10.1016/j.chaos.2020.110122
Van Ginneken B, Ter Haar Romeny BM, Viergever MA (2001) Computer-aided diagnosis in chest radiography: a survey. IEEE Trans Med Imaging 20(12):1228–1241. https://doi.org/10.1109/42.974918
Varela-Santos S, Melin P (2021) A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks. Inf Sci 545:403–414. https://doi.org/10.1016/j.ins.2020.09.041
Varuna Shree N, Kumar TNR (2018) Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Inform 5(1):23–30. https://doi.org/10.1007/s40708-017-0075-5
Wahba MA, Ashour AS, Guo Y, Napoleon SA, Abd Elnaby MM (2018) A novel cumulative level difference mean based GLDM and modified ABCD features ranked using eigenvector centrality approach for four skin lesion types classification. Comput Methods Programs Biomed 165:163–174
Wang J, Bao Y, Wen Y, Lu H, Luo H, Xiang Y, Li X, Liu C, Qian D (2020a) Prior-attention residual learning for more discriminative COVID-19 screening in CT images. IEEE Trans Med Imaging 39(8):2572–2583
Wang X, Deng X, Fu Q, Zhou Q, Feng J, Ma H, Liu W, Zheng C (2020b) A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT. IEEE Trans Med Imaging 39(8):2615–2625
Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, Cai M, Yang J, Li Y, Meng X, Xu B (2021) A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). Eur Radiol 31:6096–6104
Worldometer. https://www.worldometers.info/coronavirus/. Accessed 13 June 2022
Wu S (2021a) Expression recognition method using improved VGG-16 network model in robot interaction. J Robotics. https://doi.org/10.1155/2021/9326695
Wu X, Chen C, Zhong M, Wang J, Shi J (2021b) COVID-AL: the diagnosis of COVID-19 with deep active learning. Med Image Anal 68:101913
Yuan C, Chen X, Yu P, Meng R, Cheng W, Wu QMJ, Sun X (2020) Semi-supervised stacked autoencoder-based deep hierarchical semantic feature for real-time fingerprint liveness detection. J Real-Time Image Proc 17(1):55–71. https://doi.org/10.1007/s11554-019-00928-0
Zargari Khuzani A, Heidari M, Shariati SA (2021) COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images. Sci Rep 11(1):1–6. https://doi.org/10.1038/s41598-021-88807-2
Zhang C, Cheng X, Liu J, He J, Liu G (2018) Deep sparse autoencoder for feature extraction and diagnosis of locomotive adhesion status. J Control Sci Eng. https://doi.org/10.1155/2018/8676387
Zhang J, Xie Y, Pang G, Liao Z, Verjans J, Li W, Sun Z, He J, Li Y, Shen C, Xia Y (2021a) Viral pneumonia screening on chest X-rays using confidence-aware anomaly detection. IEEE Trans Med Imaging 40(3):879–890. https://doi.org/10.1109/TMI.2020.3040950
Zhang L, Shen B, Barnawi A, Xi S, Kumar N, Wu Y (2021b) FedDPGAN: federated differentially private generative adversarial networks framework for the detection of COVID-19 pneumonia. Inform Syst Front 23(6):1403–1415
Zhang W, Zhou T, Lu Q, Wang X, Zhu C, Sun H, Wang Z, Lo SK, Wang FY (2021c) Dynamic-fusion-based federated learning for COVID-19 detection. IEEE Internet Things J 8(21):15884–15891
Zheng W, Yan L, Gou C, Zhang ZC, Zhang JJ, Hu M, Wang FY (2021) Learning to learn by yourself: unsupervised meta-learning with self-knowledge distillation for COVID-19 diagnosis from pneumonia cases. Int J Intell Syst 36(8):4033–4064
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Prity, F.S., Nath, N., Nath, A. et al. Neural network-based strategies for automatically diagnosing of COVID-19 from X-ray images utilizing different feature extraction algorithms. Netw Model Anal Health Inform Bioinforma 12, 28 (2023). https://doi.org/10.1007/s13721-023-00423-4
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DOI: https://doi.org/10.1007/s13721-023-00423-4